Overview

Dataset statistics

Number of variables15
Number of observations400
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory47.0 KiB
Average record size in memory120.3 B

Variable types

Numeric15

Warnings

operating-margin is highly correlated with ebit-margin and 2 other fieldsHigh correlation
ebit-margin is highly correlated with operating-margin and 2 other fieldsHigh correlation
pre-tax-profit-margin is highly correlated with operating-margin and 2 other fieldsHigh correlation
net-profit-margin is highly correlated with operating-margin and 2 other fieldsHigh correlation
roa is highly correlated with roiHigh correlation
roi is highly correlated with roaHigh correlation
pre-tax-profit-margin has unique values Unique
days-sales-in-receivables has unique values Unique
roe has unique values Unique
roa has unique values Unique
roi has unique values Unique
book-value-per-share has unique values Unique
sigma has unique values Unique

Reproduction

Analysis started2021-05-21 10:08:04.681230
Analysis finished2021-05-21 10:08:55.247859
Duration50.57 seconds
Software versionpandas-profiling v2.11.0
Download configurationconfig.yaml

Variables

current-ratio
Real number (ℝ≥0)

Distinct395
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.982951
Minimum0.6271
Maximum10.2911
Zeros0
Zeros (%)0.0%
Memory size3.2 KiB
2021-05-21T15:38:55.403779image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.6271
5-th percentile0.79447
Q11.054725
median1.55835
Q32.2316
95-th percentile5.87699
Maximum10.2911
Range9.664
Interquartile range (IQR)1.176875

Descriptive statistics

Standard deviation1.575446122
Coefficient of variation (CV)0.7944957398
Kurtosis7.652407119
Mean1.982951
Median Absolute Deviation (MAD)0.52235
Skewness2.649002201
Sum793.1804
Variance2.482030482
MonotocityNot monotonic
2021-05-21T15:38:55.641197image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.07432
 
0.5%
1.83572
 
0.5%
0.77282
 
0.5%
2.51582
 
0.5%
1.56322
 
0.5%
0.87971
 
0.2%
2.06711
 
0.2%
1.50921
 
0.2%
0.89011
 
0.2%
1.24251
 
0.2%
Other values (385)385
96.2%
ValueCountFrequency (%)
0.62711
0.2%
0.70611
0.2%
0.72471
0.2%
0.73291
0.2%
0.73361
0.2%
ValueCountFrequency (%)
10.29111
0.2%
8.98481
0.2%
8.64921
0.2%
8.54251
0.2%
8.42031
0.2%

gross-margin
Real number (ℝ)

Distinct321
Distinct (%)80.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55.4188015
Minimum-17.4856
Maximum100
Zeros0
Zeros (%)0.0%
Memory size3.2 KiB
2021-05-21T15:38:55.880614image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-17.4856
5-th percentile21.304105
Q134.9126
median51.3918
Q367.125475
95-th percentile100
Maximum100
Range117.4856
Interquartile range (IQR)32.212875

Descriptive statistics

Standard deviation26.95559208
Coefficient of variation (CV)0.4863979616
Kurtosis-0.8187519426
Mean55.4188015
Median Absolute Deviation (MAD)16.1632
Skewness0.4338428314
Sum22167.5206
Variance726.6039446
MonotocityNot monotonic
2021-05-21T15:38:56.125345image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10080
 
20.0%
40.98111
 
0.2%
48.50441
 
0.2%
60.38981
 
0.2%
25.35061
 
0.2%
67.35521
 
0.2%
67.0441
 
0.2%
53.13751
 
0.2%
48.80941
 
0.2%
64.49931
 
0.2%
Other values (311)311
77.8%
ValueCountFrequency (%)
-17.48561
0.2%
7.78761
0.2%
12.46421
0.2%
13.35871
0.2%
13.39281
0.2%
ValueCountFrequency (%)
10080
20.0%
78.64921
 
0.2%
78.25811
 
0.2%
77.29651
 
0.2%
76.94781
 
0.2%

operating-margin
Real number (ℝ)

HIGH CORRELATION

Distinct399
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.56488675
Minimum-398.3604
Maximum69.4912
Zeros0
Zeros (%)0.0%
Memory size3.2 KiB
2021-05-21T15:38:56.368112image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-398.3604
5-th percentile-10.92967
Q15.353525
median23.2668
Q334.18205
95-th percentile63.546
Maximum69.4912
Range467.8516
Interquartile range (IQR)28.828525

Descriptive statistics

Standard deviation39.28756187
Coefficient of variation (CV)1.910419559
Kurtosis47.57513121
Mean20.56488675
Median Absolute Deviation (MAD)16.75975
Skewness-5.497901204
Sum8225.9547
Variance1543.512517
MonotocityNot monotonic
2021-05-21T15:38:56.586165image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.7742
 
0.5%
15.13651
 
0.2%
34.22841
 
0.2%
47.44061
 
0.2%
62.67941
 
0.2%
31.88621
 
0.2%
-101.63741
 
0.2%
62.00121
 
0.2%
37.42471
 
0.2%
5.12241
 
0.2%
Other values (389)389
97.2%
ValueCountFrequency (%)
-398.36041
0.2%
-294.23871
0.2%
-216.46981
0.2%
-205.52631
0.2%
-111.81111
0.2%
ValueCountFrequency (%)
69.49121
0.2%
68.42861
0.2%
67.59281
0.2%
67.50821
0.2%
67.2091
0.2%

ebit-margin
Real number (ℝ)

HIGH CORRELATION

Distinct399
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.5648865
Minimum-398.3604
Maximum69.4912
Zeros0
Zeros (%)0.0%
Memory size3.2 KiB
2021-05-21T15:38:56.800943image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-398.3604
5-th percentile-10.92967
Q15.353525
median23.2668
Q334.18205
95-th percentile63.546
Maximum69.4912
Range467.8516
Interquartile range (IQR)28.828525

Descriptive statistics

Standard deviation39.28756211
Coefficient of variation (CV)1.910419594
Kurtosis47.57512982
Mean20.5648865
Median Absolute Deviation (MAD)16.75975
Skewness-5.4979011
Sum8225.9546
Variance1543.512537
MonotocityNot monotonic
2021-05-21T15:38:57.012279image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.7742
 
0.5%
15.13651
 
0.2%
34.22841
 
0.2%
47.44061
 
0.2%
62.67941
 
0.2%
31.88621
 
0.2%
-101.63741
 
0.2%
62.00121
 
0.2%
37.42471
 
0.2%
5.12241
 
0.2%
Other values (389)389
97.2%
ValueCountFrequency (%)
-398.36041
0.2%
-294.23871
0.2%
-216.46981
0.2%
-205.52631
0.2%
-111.81111
0.2%
ValueCountFrequency (%)
69.49121
0.2%
68.42861
0.2%
67.59281
0.2%
67.50821
0.2%
67.2091
0.2%

pre-tax-profit-margin
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct400
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.3879275
Minimum-395.8054
Maximum74.0253
Zeros0
Zeros (%)0.0%
Memory size3.2 KiB
2021-05-21T15:38:57.231320image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-395.8054
5-th percentile-13.848125
Q14.86755
median23.77435
Q335.315225
95-th percentile63.50373
Maximum74.0253
Range469.8307
Interquartile range (IQR)30.447675

Descriptive statistics

Standard deviation39.5869642
Coefficient of variation (CV)1.94168653
Kurtosis45.97865789
Mean20.3879275
Median Absolute Deviation (MAD)17.4303
Skewness-5.403692118
Sum8155.171
Variance1567.127734
MonotocityNot monotonic
2021-05-21T15:38:57.447258image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
53.46151
 
0.2%
3.75381
 
0.2%
2.17511
 
0.2%
18.68311
 
0.2%
42.05621
 
0.2%
30.67271
 
0.2%
2.0291
 
0.2%
57.98141
 
0.2%
37.78421
 
0.2%
-16.08091
 
0.2%
Other values (390)390
97.5%
ValueCountFrequency (%)
-395.80541
0.2%
-297.72271
0.2%
-220.91651
0.2%
-206.66921
0.2%
-112.70251
0.2%
ValueCountFrequency (%)
74.02531
0.2%
67.31941
0.2%
66.94051
0.2%
66.92761
0.2%
66.88351
0.2%

net-profit-margin
Real number (ℝ)

HIGH CORRELATION

Distinct399
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.81416725
Minimum-396.2143
Maximum60.8568
Zeros0
Zeros (%)0.0%
Memory size3.2 KiB
2021-05-21T15:38:57.665071image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-396.2143
5-th percentile-15.67375
Q13.221875
median18.99775
Q329.674275
95-th percentile47.591035
Maximum60.8568
Range457.0711
Interquartile range (IQR)26.4524

Descriptive statistics

Standard deviation36.64438831
Coefficient of variation (CV)2.473604334
Kurtosis59.3989207
Mean14.81416725
Median Absolute Deviation (MAD)14.69235
Skewness-6.506276267
Sum5925.6669
Variance1342.811195
MonotocityNot monotonic
2021-05-21T15:38:57.881998image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.65652
 
0.5%
49.05931
 
0.2%
2.76891
 
0.2%
19.92761
 
0.2%
17.36391
 
0.2%
25.80341
 
0.2%
-20.75191
 
0.2%
1.36921
 
0.2%
20.80031
 
0.2%
3.31731
 
0.2%
Other values (389)389
97.2%
ValueCountFrequency (%)
-396.21431
0.2%
-297.91831
0.2%
-221.1481
0.2%
-206.95621
0.2%
-112.85331
0.2%
ValueCountFrequency (%)
60.85681
0.2%
54.96751
0.2%
54.4461
0.2%
54.18641
0.2%
54.06831
0.2%

asset-turnover
Real number (ℝ≥0)

Distinct366
Distinct (%)91.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.22724
Minimum0.0343
Maximum0.6896
Zeros0
Zeros (%)0.0%
Memory size3.2 KiB
2021-05-21T15:38:58.092943image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.0343
5-th percentile0.071395
Q10.130775
median0.1588
Q30.23675
95-th percentile0.598715
Maximum0.6896
Range0.6553
Interquartile range (IQR)0.105975

Descriptive statistics

Standard deviation0.1652782615
Coefficient of variation (CV)0.7273290858
Kurtosis0.6884118543
Mean0.22724
Median Absolute Deviation (MAD)0.03595
Skewness1.440443516
Sum90.896
Variance0.02731690371
MonotocityNot monotonic
2021-05-21T15:38:58.313209image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.14764
 
1.0%
0.15833
 
0.8%
0.12623
 
0.8%
0.12412
 
0.5%
0.12712
 
0.5%
0.16192
 
0.5%
0.12792
 
0.5%
0.1562
 
0.5%
0.13212
 
0.5%
0.13742
 
0.5%
Other values (356)376
94.0%
ValueCountFrequency (%)
0.03431
0.2%
0.03961
0.2%
0.05521
0.2%
0.05681
0.2%
0.06191
0.2%
ValueCountFrequency (%)
0.68961
0.2%
0.66631
0.2%
0.65851
0.2%
0.65331
0.2%
0.64971
0.2%

receiveable-turnover
Real number (ℝ≥0)

Distinct399
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.956246
Minimum0.9484
Maximum26.9501
Zeros0
Zeros (%)0.0%
Memory size3.2 KiB
2021-05-21T15:38:58.550061image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.9484
5-th percentile1.432585
Q11.900975
median2.76285
Q34.3712
95-th percentile22.12584
Maximum26.9501
Range26.0017
Interquartile range (IQR)2.470225

Descriptive statistics

Standard deviation5.953220141
Coefficient of variation (CV)1.201155096
Kurtosis4.569656767
Mean4.956246
Median Absolute Deviation (MAD)0.98965
Skewness2.422237982
Sum1982.4984
Variance35.44083004
MonotocityNot monotonic
2021-05-21T15:38:58.772566image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.90092
 
0.5%
3.86211
 
0.2%
2.08021
 
0.2%
7.11121
 
0.2%
1.74851
 
0.2%
1.13611
 
0.2%
1.87391
 
0.2%
3.41721
 
0.2%
2.24041
 
0.2%
2.11881
 
0.2%
Other values (389)389
97.2%
ValueCountFrequency (%)
0.94841
0.2%
1.01041
0.2%
1.13611
0.2%
1.13781
0.2%
1.14151
0.2%
ValueCountFrequency (%)
26.95011
0.2%
26.85861
0.2%
26.76911
0.2%
25.59541
0.2%
24.27271
0.2%

days-sales-in-receivables
Real number (ℝ≥0)

UNIQUE

Distinct400
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.66710525
Minimum3.3395
Maximum94.9016
Zeros0
Zeros (%)0.0%
Memory size3.2 KiB
2021-05-21T15:38:58.992979image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum3.3395
5-th percentile4.067695
Q120.592425
median32.5757
Q347.343125
95-th percentile62.822745
Maximum94.9016
Range91.5621
Interquartile range (IQR)26.7507

Descriptive statistics

Standard deviation18.35903587
Coefficient of variation (CV)0.5453107933
Kurtosis-0.190673861
Mean33.66710525
Median Absolute Deviation (MAD)13.32395
Skewness0.3377804252
Sum13466.8421
Variance337.0541981
MonotocityNot monotonic
2021-05-21T15:38:59.228263image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
49.98341
 
0.2%
4.16871
 
0.2%
4.60471
 
0.2%
17.59211
 
0.2%
59.53681
 
0.2%
57.06381
 
0.2%
46.3551
 
0.2%
36.711
 
0.2%
89.07531
 
0.2%
3.94621
 
0.2%
Other values (390)390
97.5%
ValueCountFrequency (%)
3.33951
0.2%
3.35091
0.2%
3.36211
0.2%
3.51631
0.2%
3.70791
0.2%
ValueCountFrequency (%)
94.90161
0.2%
89.07531
0.2%
79.21861
0.2%
79.09831
0.2%
78.84361
0.2%

roe
Real number (ℝ)

UNIQUE

Distinct400
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.301302
Minimum-169.7387
Maximum397.5031
Zeros0
Zeros (%)0.0%
Memory size3.2 KiB
2021-05-21T15:38:59.457554image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-169.7387
5-th percentile-7.80984
Q13.65025
median5.57305
Q38.9195
95-th percentile18.958325
Maximum397.5031
Range567.2418
Interquartile range (IQR)5.26925

Descriptive statistics

Standard deviation23.67965577
Coefficient of variation (CV)3.757898887
Kurtosis194.8831038
Mean6.301302
Median Absolute Deviation (MAD)2.59155
Skewness10.17571422
Sum2520.5208
Variance560.7260976
MonotocityNot monotonic
2021-05-21T15:38:59.667495image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.20291
 
0.2%
6.86611
 
0.2%
19.39581
 
0.2%
7.89561
 
0.2%
4.47031
 
0.2%
2.28641
 
0.2%
10.0211
 
0.2%
0.81621
 
0.2%
11.7381
 
0.2%
21.85291
 
0.2%
Other values (390)390
97.5%
ValueCountFrequency (%)
-169.73871
0.2%
-72.11951
0.2%
-58.40691
0.2%
-36.37171
0.2%
-29.5651
0.2%
ValueCountFrequency (%)
397.50311
0.2%
43.42081
0.2%
42.37191
0.2%
40.67531
0.2%
35.87671
0.2%

return-on-tangible-equity
Real number (ℝ)

Distinct399
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-4.536191
Minimum-3572.881
Maximum796.3158
Zeros0
Zeros (%)0.0%
Memory size3.2 KiB
2021-05-21T15:38:59.901778image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-3572.881
5-th percentile-31.86591
Q1-1.06655
median7.0011
Q313.4391
95-th percentile33.19967
Maximum796.3158
Range4369.1968
Interquartile range (IQR)14.50565

Descriptive statistics

Standard deviation214.6997191
Coefficient of variation (CV)-47.33039661
Kurtosis204.9761408
Mean-4.536191
Median Absolute Deviation (MAD)6.9121
Skewness-12.89039543
Sum-1814.4764
Variance46095.9694
MonotocityNot monotonic
2021-05-21T15:39:00.127823image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.37732
 
0.5%
-11.6711
 
0.2%
9.05781
 
0.2%
30.36241
 
0.2%
11.88921
 
0.2%
796.31581
 
0.2%
-31.8311
 
0.2%
6.97821
 
0.2%
1.11581
 
0.2%
22.76181
 
0.2%
Other values (389)389
97.2%
ValueCountFrequency (%)
-3572.8811
0.2%
-1801.0641
0.2%
-793.33331
0.2%
-169.73871
0.2%
-72.11951
0.2%
ValueCountFrequency (%)
796.31581
0.2%
734.05021
0.2%
438.41341
0.2%
397.50311
0.2%
152.49791
0.2%

roa
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct400
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.61062575
Minimum-13.6934
Maximum10.8552
Zeros0
Zeros (%)0.0%
Memory size3.2 KiB
2021-05-21T15:39:00.357842image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-13.6934
5-th percentile-2.314275
Q11.56215
median2.79615
Q34.03705
95-th percentile6.84724
Maximum10.8552
Range24.5486
Interquartile range (IQR)2.4749

Descriptive statistics

Standard deviation3.015265593
Coefficient of variation (CV)1.154997262
Kurtosis6.945147229
Mean2.61062575
Median Absolute Deviation (MAD)1.24285
Skewness-1.597141748
Sum1044.2503
Variance9.091826594
MonotocityNot monotonic
2021-05-21T15:39:00.869382image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-3.07091
 
0.2%
2.85471
 
0.2%
2.82231
 
0.2%
2.82141
 
0.2%
1.11061
 
0.2%
1.54711
 
0.2%
2.30821
 
0.2%
1.84391
 
0.2%
3.5931
 
0.2%
3.47171
 
0.2%
Other values (390)390
97.5%
ValueCountFrequency (%)
-13.69341
0.2%
-13.59341
0.2%
-11.80831
0.2%
-11.42191
0.2%
-9.29351
0.2%
ValueCountFrequency (%)
10.85521
0.2%
9.95461
0.2%
9.42021
0.2%
9.00641
0.2%
8.54681
0.2%

roi
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct400
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.0405945
Minimum-28.2224
Maximum17.3741
Zeros0
Zeros (%)0.0%
Memory size3.2 KiB
2021-05-21T15:39:01.087713image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-28.2224
5-th percentile-4.370245
Q12.6439
median4.1806
Q36.05745
95-th percentile11.838295
Maximum17.3741
Range45.5965
Interquartile range (IQR)3.41355

Descriptive statistics

Standard deviation5.08315243
Coefficient of variation (CV)1.258020925
Kurtosis7.85851712
Mean4.0405945
Median Absolute Deviation (MAD)1.71455
Skewness-1.584974949
Sum1616.2378
Variance25.83843863
MonotocityNot monotonic
2021-05-21T15:39:01.316051image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.53851
 
0.2%
3.18661
 
0.2%
5.86861
 
0.2%
3.73071
 
0.2%
3.69251
 
0.2%
3.86571
 
0.2%
-2.21111
 
0.2%
11.92371
 
0.2%
1.34191
 
0.2%
2.85771
 
0.2%
Other values (390)390
97.5%
ValueCountFrequency (%)
-28.22241
0.2%
-22.8041
0.2%
-18.04981
0.2%
-16.77351
0.2%
-16.45551
0.2%
ValueCountFrequency (%)
17.37411
0.2%
16.94421
0.2%
16.58541
0.2%
16.29481
0.2%
15.94891
0.2%

book-value-per-share
Real number (ℝ)

UNIQUE

Distinct400
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.09056525
Minimum-0.0527
Maximum205
Zeros0
Zeros (%)0.0%
Memory size3.2 KiB
2021-05-21T15:39:01.549432image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-0.0527
5-th percentile2.63864
Q16.352025
median11.64265
Q321.305525
95-th percentile31.44408
Maximum205
Range205.0527
Interquartile range (IQR)14.9535

Descriptive statistics

Standard deviation22.44877449
Coefficient of variation (CV)1.31351855
Kurtosis30.88442767
Mean17.09056525
Median Absolute Deviation (MAD)6.15495
Skewness5.030760869
Sum6836.2261
Variance503.9474762
MonotocityNot monotonic
2021-05-21T15:39:01.759675image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22.45221
 
0.2%
15.62591
 
0.2%
10.80911
 
0.2%
17.2591
 
0.2%
1.64921
 
0.2%
24.22021
 
0.2%
0.42871
 
0.2%
5.08081
 
0.2%
16.91811
 
0.2%
22.55111
 
0.2%
Other values (390)390
97.5%
ValueCountFrequency (%)
-0.05271
0.2%
0.11811
0.2%
0.21841
0.2%
0.29261
0.2%
0.29281
0.2%
ValueCountFrequency (%)
2051
0.2%
185.69381
0.2%
164.89041
0.2%
147.16171
0.2%
130.80561
0.2%

sigma
Real number (ℝ≥0)

UNIQUE

Distinct400
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.01870601091
Minimum0.001572467
Maximum0.123591654
Zeros0
Zeros (%)0.0%
Memory size3.2 KiB
2021-05-21T15:39:01.979491image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.001572467
5-th percentile0.0047460897
Q10.0094064075
median0.014945543
Q30.0234071265
95-th percentile0.0443983999
Maximum0.123591654
Range0.122019187
Interquartile range (IQR)0.014000719

Descriptive statistics

Standard deviation0.01462320384
Coefficient of variation (CV)0.7817382291
Kurtosis13.64567891
Mean0.01870601091
Median Absolute Deviation (MAD)0.006702446
Skewness2.858416894
Sum7.482404362
Variance0.0002138380905
MonotocityNot monotonic
2021-05-21T15:39:02.229361image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0582751241
 
0.2%
0.01445041
 
0.2%
0.0244730491
 
0.2%
0.0145370541
 
0.2%
0.0087685941
 
0.2%
0.0074117451
 
0.2%
0.0257190271
 
0.2%
0.0160022441
 
0.2%
0.0111009971
 
0.2%
0.0210617241
 
0.2%
Other values (390)390
97.5%
ValueCountFrequency (%)
0.0015724671
0.2%
0.001805681
0.2%
0.0023030751
0.2%
0.002677461
0.2%
0.0029361281
0.2%
ValueCountFrequency (%)
0.1235916541
0.2%
0.1175217551
0.2%
0.0913635691
0.2%
0.0832936381
0.2%
0.0702014551
0.2%

Interactions

2021-05-21T15:38:12.822311image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:13.192015image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:13.427317image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:13.637828image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:13.852084image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:14.061047image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:14.329277image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:14.576097image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:14.821922image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:15.041155image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:15.260647image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:15.457150image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:15.664400image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:15.865324image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:16.078533image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:16.289745image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:16.512858image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:16.740238image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:17.014664image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:17.251004image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:17.576055image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:17.840845image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:18.081956image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:18.332166image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:18.566900image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:18.773597image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:18.979542image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:19.180511image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:19.393558image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:19.572872image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:19.764498image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:19.958369image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:20.158955image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:20.338893image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:20.534678image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:20.724895image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:20.904537image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:21.083641image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:21.267710image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:21.444023image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:21.622915image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:21.798313image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:21.985992image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:22.160098image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:22.342932image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:22.519681image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:22.690572image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:22.867104image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:23.054395image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:23.242527image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:23.421690image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:23.600405image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:23.891303image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:24.066572image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:24.253663image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:24.428460image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:24.617496image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:24.806088image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:24.998417image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:25.180870image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:25.356685image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:25.538484image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:25.728189image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:25.913231image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:26.081817image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:26.260519image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:26.440006image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:26.608349image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:26.790047image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:26.966901image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:27.158864image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:27.335052image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:27.519288image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:27.702207image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:27.880067image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:28.061045image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:28.255254image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:28.439825image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:28.621506image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:28.802651image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:28.983098image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:29.154149image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:29.334632image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:29.513497image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:29.701976image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:29.900802image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:30.118215image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:30.315694image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:30.508470image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:30.705429image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:30.900369image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:31.250968image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:31.440108image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:31.637219image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:31.836089image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:32.024701image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:32.220728image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:32.415142image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:32.623205image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:32.802715image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:33.008467image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:33.196683image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:33.385173image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:33.579755image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:33.769642image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:33.967269image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:34.156260image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:34.344691image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:34.532043image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:34.705169image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:34.887135image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:35.081631image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:35.284933image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:35.468169image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:35.653923image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:35.829695image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:36.001258image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:36.188744image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:36.380145image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:36.604454image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:36.828386image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:37.039083image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:37.249992image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:37.470050image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:37.695248image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:37.972581image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:38.231459image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:38.426462image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:38.619641image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:38.805966image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:38.984594image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:39.173368image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:39.357989image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:39.558241image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:39.756946image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:39.947874image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:40.128731image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:40.305779image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:40.659723image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:40.836863image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:41.034038image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:41.209897image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:41.399096image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:41.575427image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:41.752499image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-05-21T15:38:42.839389image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:43.012598image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-05-21T15:38:44.839227image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:45.019876image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-05-21T15:38:45.773248image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:45.979008image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:46.206027image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-05-21T15:38:47.141258image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-05-21T15:38:48.034663image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:48.208334image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-05-21T15:38:51.087981image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-05-21T15:38:52.486026image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:52.684343image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:52.890071image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:53.083090image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:53.278461image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:53.479487image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:53.682995image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:53.875092image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-21T15:38:54.068926image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-05-21T15:39:02.460235image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-05-21T15:39:02.887875image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-05-21T15:39:03.316100image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-05-21T15:39:03.746183image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-05-21T15:38:54.463220image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-05-21T15:38:55.005653image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

current-ratiogross-marginoperating-marginebit-marginpre-tax-profit-marginnet-profit-marginasset-turnoverreceiveable-turnoverdays-sales-in-receivablesroereturn-on-tangible-equityroaroibook-value-per-sharesigma
01.141742.506530.700830.700831.267926.37750.26572.711733.189434.158334.15837.008613.28874.14580.017064
11.163039.777830.091830.091830.132225.80340.31481.901047.342543.420843.42088.121617.37413.93650.016324
21.363638.160422.836922.836923.031619.58790.19981.727852.088919.395819.39583.91287.72723.84870.023187
31.469537.999521.933521.933522.010618.85400.18811.860848.366415.568215.56823.54606.76554.21820.033035
41.496038.361922.041422.041422.525019.29070.18201.900947.346714.343614.34363.51096.71544.53430.011731
51.597838.354827.847227.847228.227324.21720.26962.298639.154624.836124.83616.528112.17685.10440.033503
61.540137.965324.398824.398825.182721.37100.18921.398164.371615.124715.12474.04297.50765.09130.011626
71.504637.590421.453721.453722.135718.66600.16702.032544.280010.413010.41303.11695.53725.32150.027358
81.315437.612723.123323.123323.774919.92760.16962.207740.765710.921010.92103.38045.89665.74420.020733
91.300637.991927.690727.690728.354923.68050.22562.279839.476816.935016.93505.34229.46746.23130.015753

Last rows

current-ratiogross-marginoperating-marginebit-marginpre-tax-profit-marginnet-profit-marginasset-turnoverreceiveable-turnoverdays-sales-in-receivablesroereturn-on-tangible-equityroaroibook-value-per-sharesigma
3902.259562.404525.988325.988329.103321.87940.14893.625424.82465.31997.45873.25784.299711.15060.003602
3912.550558.309321.224021.224021.021015.61160.14963.687724.40563.71475.23352.33482.985510.80910.006612
3922.430756.168520.023920.023919.419716.25600.15143.557725.29733.99465.73942.46143.178610.34640.008104
3932.431258.002523.410323.410323.773818.31270.15983.516025.59694.82006.99982.92593.835910.35660.011542
3941.925463.275628.542828.542829.137322.08520.18083.417226.33726.03228.89153.99245.27249.88940.016628
3952.446963.358328.383128.383129.138620.93920.18663.809523.62495.79558.47353.90735.05989.73050.008307
3962.128964.040029.521229.521229.552221.21490.17973.196928.15205.85548.74543.81255.08479.34080.013536
3972.151064.463233.117333.117333.030924.19530.19533.804723.65527.318511.05634.72456.34029.18220.012692
3982.241863.359833.619133.619134.370824.36590.20173.724924.16157.520011.35784.91566.51979.01070.016925
3992.231260.635430.194930.194930.164222.66730.19723.879723.19756.06288.98364.46975.81359.21910.021729